A strictly local, privacy-first RAG (Retrieval Augmented Generation) backend. It allows you to ingest PDF/TXT documents and chat with them using Ollama and ChromaDB.
Status: core-logic
- FastAPI: Async endpoints for
/ingestand/chat. - ChromaDB: Local vector storage.
- Ollama: Uses local LLMs (Llama 3, Mistral, etc).
- Privacy: No data leaves your machine.
Since this is the source version, you must manage the infrastructure yourself:
- Install Ollama: Download here and run
ollama serve. - Install ChromaDB: You must run a local Chroma instance or install the python client.
- Python 3.10+: Ensure your venv is active.
# 1. Install Dependencies
pip install -r requirements.txt
# 2. Pull the Embedding Model (Manual)
ollama pull llama3
ollama pull nomic-embed-text
# 3. Run the API
uvicorn src.main:app --reloadI maintain a Production-Ready Starter Kit that includes:
β Full Docker Compose (Orchestrates API + Chroma + Ollama).
β Production Dockerfile (Optimized, lightweight).
β Environment Configs (Pre-set for Llama 3).
β One-Command Run (docker-compose up).